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AI Engineer with less than a year in Machine Learning, NLP, and Data Analytics
Final-year Computer Science student with strong expertise in Machine Learning, Natural Language Processing, and Data Analytics. Experienced in building end-to-end AI systems including transformer-based NLP models, deep learning pipelines, and scalable inference APIs. Proficient in Python, PyTorch, TensorFlow, and SQL with hands-on experience deploying ML models using FastAPI and building interactive dashboards with Streamlit.
St. Patrick's Junior College
Intermediate (IPE)
June 1, 2021 – May 31, 2023
St. Ann's Girls High School
SSC
June 1, 2020 – May 31, 2021
KL University
Bachelor of Technology · Computer Science and Engineering
N/A – June 30, 2027
AI-Powered Legal Document Summarization & Q&A System
May 1, 2026 – Present
Led the AI/NLP development by implementing a Transformer-based summarization pipeline using T5-small for condensing large legal documents. Designed and built a Retrieval-Augmented Generation (RAG) system using FAISS for context-aware question answering. Developed text preprocessing and document parsing pipelines for PDF, DOCX, and TXT formats. Implemented semantic search using vector embeddings to improve relevance of retrieved legal context. Integrated AI models with Flask APIs for real-time inference and query handling. Collaborated with team on frontend and deployment while owning core AI model logic and optimization.
AI-Powered Chest X-Ray Disease Detection System
May 1, 2026 – Present
Developed a deep learning model using ResNet-18 to classify chest X-rays into NORMAL, PNEUMONIA, and TUBERCULOSIS. Owned the model pipeline including data preprocessing, label cleaning, and multi-dataset integration. Implemented image preprocessing techniques such as resizing, normalization, and transformations using PyTorch. Fine-tuned a pretrained ResNet-18 model for multi-class classification. Built an inference pipeline to generate predictions with confidence scores for real-time classification. Integrated the trained model into a Flask-based web application for user interaction.
Transformer-Based Twitter Sentiment Intelligence System
May 1, 2026 – Present
Built an NLP pipeline processing 80K+ tweets using BERT for contextual sentiment classification. Compared TF-IDF + Logistic Regression, LSTM, and BERT models to evaluate performance across approaches. Developed a custom PyTorch training pipeline with tokenization, batching, and model optimization. Deployed the model using FastAPI and built a Streamlit dashboard for real-time predictions.
Building RAG Agents with LLMs
NVIDIA
May 1, 2026 – Present
Spring Essentials
Infosys
May 1, 2026 – Present
Linguaskill Certification
Cambridge English
May 1, 2026 – Present
Convolutional Neural Networks in TensorFlow
DeepLearning.AΙ
May 1, 2026 – Present
MongoDB Certified Associate Developer
MongoDB
May 1, 2026 – Present
Advanced Automation Professional
Automation Anywhere
May 1, 2026 – Present
Introduction to TensorFlow for AI, ML, and Deep Learning
DeepLearning.AI
May 1, 2026 – Present
Data Structures and Algorithms
Infosys Springboard
May 1, 2026 – Present
Cultural Fit Analysis
The candidate exhibits a strong cultural fit for an AI Engineer role due to their diverse academic projects spanning NLP (legal summarization, sentiment analysis) and Computer Vision (medical imaging). This breadth showcases adaptability and a genuine interest in applying AI to various domains. Their proactive pursuit of certifications in cutting-edge AI topics like RAG and TensorFlow further highlights a commitment to continuous learning and staying current with industry trends, which is highly valued in dynamic tech environments. The 'Column Writer' activity also suggests good communication skills and a broader engagement beyond core technical tasks.
Soft Skills & Operational Fit
The candidate demonstrates strong operational fit through hands-on experience with model deployment using Flask and FastAPI, and version control with Git/GitHub. Soft skills include demonstrated leadership in AI/NLP development, ownership of model pipelines, and collaboration with teams on frontend and deployment, indicating a capacity for teamwork and responsibility.